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Mathematische Modellierung und Datenanalyse

Fachbereich 8.4

Publikations Einzelansicht


Titel: Linear Mixed Models: Gum and Beyond
Autor(en): B. Arendacká, A. Täubner, S. Eichstädt, T. Bruns and C. Elster
Journal: Measurement Science Review
Jahr: 2014
Band: 14
Ausgabe: 2
Seite(n): 52-61
DOI: 10.2478/msr-2014-0009
ISSN: 1335-8871
Datei / URL: fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/epjconf_icm2014_00003.pdf
Web URL: http://www.degruyter.com/view/j/msr.2014.14.issue-2/msr-2014-0009/msr-2014-0009.xml
Schlüsselwörter: dynamic measurement, acceleration, dynamic calibration, mixed model, design of experiment
Marker: 8.42, Dynamik, Unsicherheit
Zusammenfassung: In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneficial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers.

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